检索结果-内蒙古大学图书馆

A density algorithm for controller placement problem in software defined wide area networks

JOURNAL OF SUPERCOMPUTING 2023年第5期79卷 5374-5402页

作者： He, Dun Chen, Jue Qiu, Xihe Shanghai Univ Engn Sci Sch Elect & Elect Engn 333 Longteng Rd Shanghai 201620 Peoples R China

Through decoupling the data plane from the control planes, the Software Defined Network (SDN) improves the network flexibility and has gained much attention from both academia and industry. In order to apply to Wide Area Network (WAN), the logically centralized and physically distributed multi-controller network architecture is proposed. In this situation, how many controllers are required and where they should be placed is a urgent problem to be solved, which is called the controller placement problem (CPP). This paper discusses the joint optimization of latency and required number of controllers considering revenue cost and network architecture. We propose a Density-based controller placement Algorithm (DCPA), which can obtain the optimal number of controllers and then divides the entire network into multiple sub-networks adaptively. In each sub-network, the controllers are deployed with the purpose of minimizing the average propagation latency and the worst-case propagation latency between controllers and switches at the same time. We conduct experiments on 8 real network topologies from the OS3E and Internet Topology Zoo to evaluate the performance of algorithm. The results verify that DCPA can always find out the optimal solution with a low time consumption to reduce latency for different network scales, which reduces latency by up to 46, 11 and 7 when compared with Density-Based controller placement (DBCP), Pareto-based Optimal controller placement (POCO) and Clustering-based Network Partition Algorithm (CNPA), respectively, and reduce the load of controllers by up to 38, 20 and 13 when compared with DBCP, POCO and CNPA, respectively. As a result, our proposed DCPA can decrease the controller cost, propagation latency and controller load simultaneously when solving CPP.

关键词： SDN controller placement problem Density based clustering Propagation latency

来源：评论

学校读者我要写书评

暂无评论

controller placement problem in software-defined networking: A survey

引用

NETWORKS 2021年第2期78卷 195-223页

作者： Kumari, Abha Sairam, Ashok Singh Indian Inst Technol Patna Dept Comp Sci & Engn Patna Bihar India Indian Inst Technol Guwahati Dept Math Gauhati 781039 India

A distinctive feature of software-defined networking (SDN) is a logically centralized control plane realized using multiple physical controllers. The placement of the controllers, the so-called controller placement problem (CPP), is a crucial design issue. It influences network performance parameters such as latency, flow setup time, network availability, load balance of the controllers, and energy consumption. In this article, we illustrate the formulation of these CPP objectives. We categorize the CPP design solutions as either static or adaptive. In adaptive CPP, the solutions proposed dynamically adapt to the number of controllers required and the switch to controller mapping to varying network traffic. We further differentiate adaptive CPP as wired or wireless. The optimization strategies adopted by the papers are analyzed and grouped into five categories: exact, heuristic, meta-heuristic, clustering, and game theory. The merits and demerits of each approach are discussed. In conclusion, we outline the research challenges worth investigating.

关键词： controller placement problem distributed controller game theory heuristics integer program meta-heuristics mixed integer program multiobjective optimization SDN control plane scalability software-defined networking

来源：评论

学校读者我要写书评

暂无评论

An Opposition Learning-based Quantum Inspired Salp Swarm Optimization for The Multiobjective controller placement problem 10

An Opposition Learning-based Quantum Inspired Salp Swarm Opt...

引用

10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering, UPCON 2023

作者： Pathak, Sanjai Mani, Ashish Chatterjee, Amlan Amity University Uttar Pradesh Noida India California State University Dominguez Hills CarsonCA United States

ISBN: (纸本)9798350382471

This paper tackles the multiobjective controller placement problem in Software Defined Networks (SDN), a complex optimization challenge affecting network Quality of Service (QoS). In contrast to prior approaches focusing solely on propagation latency, our method considers propagation latency, inter-controller latency, and load balancing when allocating switches to controllers. We introduce EQSSA, a hybrid model that combines Elite Opposition Learning (EOL), Quantum Computing, and the Salp Swarm Optimization Algorithm (SSA). SSA is a powerful meta-heuristic for real-world optimization problems, while EOL adds cost-effectiveness and efficiency to problem-solving. Quantum-inspired techniques enhance exploration and exploitation compared to standard methods. The hybridization aims to maintain SSA's diversity and computing power. We formulate the multiobjective controller placement problem (MOCPP) based on Pareto optimality and employ EQSSA for evaluation. Experimental results, compared to standard SSA and tested on well-known benchmarks, affirm the effectiveness of our approach, demonstrating EQSSA's competitiveness in solving MOCPP and offering a promising solution to SDN controller placement challenges. © 2023 IEEE.

关键词： Computational Intelligence controller placement problem Multiobjective Combinatorial Optimization SDN SSA

来源：评论

学校读者我要写书评

暂无评论

GOKA: A Network Partition and Cluster Fusion Algorithm for controller placement problem in SDN

引用

JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2023年第9期32卷 2350144-2350144页

作者： Xiao, Changwei Chen, Jue Qiu, Xihe He, Dun Yin, Hanmin Shanghai Univ Engn Sci Sch Elect & Elect Engn Shanghai Peoples R China

Software Defined Networking (SDN) is a new promising network architecture, with the property of decoupling the data plane from the control plane and centralizing the network topology logically, making the network more agile than traditional networks. However, with the continuous expansion of network scales, the single-controller SDN architecture is unable to meet the performance requirements of the network. As a result, the logically centralized and physically separated SDN multi-controller architecture comes into being, and thereupon the controller placement problem (CPP) is proposed. In order to minimize the propagation latency in Wide Area Network (WAN), we propose Greedy Optimized K-means Algorithm (GOKA) which combines K-means with greedy algorithm. The main thought is to divide the network into multiple clusters, merge them greedily and iteratively until the given number of controllers is satisfied, and place a controller in each cluster through the K-means algorithm. With the purpose of proving the effectiveness of GOKA, we conduct experiments to compare with Pareto Simulated Annealing (PSA), Adaptive Bacterial Foraging Optimization (ABFO), K-means and K-means++ on 6 real topologies from the Internet Topology Zoo and Internet2 OS3E. The results demonstrate that GOKA has a better and more stable solution than other four heuristic algorithms, and can decrease the propagation latency by up to 83.3%, 70.7%, 88.6% and 64.5% in contrast to PSA, ABFO, K-means and K-means++, respectively. Moreover, the error rate between GOKA and the best solution is always less than 10%, which promises the precision of our proposed algorithm.

关键词： SDN controller placement problem latency K-means K-means plus plus

来源：评论

学校读者我要写书评

暂无评论

A Cross Entropy-Based Approach to controller placement problem with Link Failures in SDN

引用

JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2023年第14期32卷

作者： Yin, Hanmin Chen, Jue Shanghai Univ Engn Sci Sch Elect & Elect Engn Shanghai 201620 Peoples R China

The controller placement problem (CPP) is a key research topic in Software Defined Network (SDN), as the communication delay is influenced by the position of controllers and switches. On that basis, the network failures may happen occasionally, which can cause the increase of propagation latency and the reduction of network performance. As a result, it is essential to research the controller placement problem for Link Failures (CPLF). In this paper, authors propose a method based on the cross entropy to solve CPP after link failures, and adopt the Halton sequence to reduce the computation overhead of simulating link failures while guaranteeing the accuracy. In the experiments, we measure and compare the worst-case delay among three methods: our proposed cross entropy-based controller placement algorithm, the optimized controller placement algorithm and a greedy-based controller placement algorithm, and conduct experiments on six real network topologies. The experimental results verify that our proposed method can reduce the worst-case delay by 27% in comparison with GPA. Moreover, the proposed method can always find optimized controller placement schemes no matter how the network scale or the number of controller varies, with a less than 0.6% error when compared with the optimal solution.

关键词： controller placement problem software defined network security link failure cross entropy

来源：评论

学校读者我要写书评

暂无评论

MGOKA:A Multi-Objective Optimization Algorithm for controller placement problem Combining Network Partition with Cluster Fusion in Software Defined Network

引用

Wuhan University Journal of Natural Sciences 2024年第6期29卷 589-599页

作者： CHEN Jue XIAO Changwei QIU Xihe LÜ Wenjing School of Electronic and Electrical Engineering Shanghai University of Engineering ScienceShanghai 201620China

Software Defined Network(SDN)has been developed rapidly in technology and popularized in application due to its efficiency and flexibility in network *** multi-controller SDN architecture,the controller placement problem(CPP)must be solved carefully as it directly affects the whole network *** paper proposes a Multi-objective Greedy Optimized K-means Algorithm(MGOKA)to solve this problem to optimize worst-case and average delay between switches and controllers as well as synchronization delay and load balance among controllers for Wide Area Networks(WAN).MGOKA combines the process of network partition based on the K-means algorithm with cluster fusion based on the greedy algorithm and designs a normalization strategy to convert a multi-objective into a single-objective optimization *** simulation results depict that in different network scales with different numbers of controllers,the relative optimization rate of our proposed algorithm compared with K-means,K-means++,and GOKA can reach up to 101.5%,109.9%,and 79.8%,***,the error rate between MGOKA and the global optimal solution is always less than 4%.

关键词： Software Defined Network controller placement problem propagation delay load balance multi-objective optimization

来源：评论

学校读者我要写书评

暂无评论

Autonomous solution for controller placement problem of Software-Defined Networking using MuZero based intelligent agents

引用

JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2023年第10期35卷

作者： Benoudifa, Ouafae Wakrime, Abderrahim Ait Benaini, Redouane Mohammed V Univ Rabat Comp Sci Dept Fac Sci Rabat Morocco

controller placement is a critical issue in Software-Defined Networking, which has been identified as a potential approach to achieve more flexible network control and management. Deep Reinforcement Learning holds immense promise in achieving favorable outcomes through its ability to explore solution spaces and adapt to swiftly changing data streams. Furthermore, the advancement of reinforcement learning algorithms, like chess or Go, has inspired the concept of treating controller placement problem as a Markov game. These algorithms are employed to train intelligent agents capable of autonomously resolving this problem. This paper presents an intelligent system capable of optimizing the placement of controllers in Software-Defined Networking. The Muzero Reinforcement learning algorithm is used to train a model via self-competition which, by combining a tree search with a learned model, achieves superhuman performance in complex domains. Once trained, the model is integrated with an Software-Defined Networking controller, so that it is able to find the optimal placement of the game in a real network by incorporating performance metrics including latency, load, and overhead communication into the training process of intelligent agents. In order to show that our approach is feasible and efficient in practice, experiment results are provided as a benchmark.

关键词： Software-Defined Networking Artificial Intelligence controller placement problem Automated placement MuZero

来源：评论

学校读者我要写书评

暂无评论

A Modified Genetic Algorithm for controller placement problem in SDN Distributed Network 26

A Modified Genetic Algorithm for Controller Placement Proble...

引用

26th IEEE Asia-Pacific Conference on Communications {APCC)

作者： Ibrahim, Abeer A. Z. Hashim, Fazirulhisyam Sali, Aduwati Noordin, Nor K. Fadul, Saber M. E. Univ Putra Malaysia Fac Engn Dept Comp & Commun Syst Engn Serdang 43400 Malaysia Univ Putra Malaysia Fac Engn Res Ctr Excellence Wireless & Photon Network WiPN Serdang 43400 Malaysia Univ Putra Malaysia Fac Engn Dept Elect & Elect Engn Serdang 43400 Malaysia

ISBN: (纸本)9781728172545

In this paper, a framework of controller placement problem (CPP) is implemented for dynamic mapping and switch migration across controllers in SDN distributed network architecture. Then a heuristic multi-level capacitated CPP (MCCPP) is formulated to efficiently assigned each switch to a corresponding controller to improve the reliability and scalability of the SDN system. The proposed method simultaneously identifies the optimum number of controllers, minimizes the delay, and ensures load balancing among controllers. Then a Genetic Algorithm (GA) is integrated with the allocation components to improve the controller locations. The effectiveness of the GA-based MCCPP is compared against heuristics CCPP over different network topologies. The adaptive GA-based yields excellent results in latency and only requires a few iterations to attain convergence.

关键词： SDN controller placement problem optimization genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

A Grey Wolf Optimization Algorithm with its application on the controller placement problem

A Grey Wolf Optimization Algorithm with its application on t...

引用

International Symposium on Artificial Intelligence and Robotics

作者： Li, Yi Nanjing Univ Posts & Telecommun Coll Automat Nanjing Peoples R China Nanjing Univ Posts & Telecommun Coll Artificial Intelligence Nanjing Peoples R China

ISBN: (数字)9781510646131

ISBN: (纸本)9781510646131;9781510646124

The Software Defined Networks is a popular network architecture that separates control and data plane. It is completed as a software to move control plane functions from network hardware to a controller node. To optimize the consistency of the network, a controller placement problem (CPP) could be treated as an optimization problem to minimize the latency between nodes by setting their location. In this paper, as a modern Evolutionary Computation Algorithm, grey wolf optimization algorithm recently has been successfully applied into solving CPP. The experimental results demonstrate the effectiveness of the proposed algorithm.

关键词： Software Defined Networks controller placement problem Grey Wolf algorithm search ability

来源：评论

学校读者我要写书评

暂无评论

Parameter Optimization Model of Heuristic Algorithms for controller placement problem in Large-Scale SDN

引用

IEEE ACCESS 2020年 8卷 151668-151680页

作者： Li, Yi Guan, Shaopeng Zhang, Conghui Sun, Wenwen Shandong Technol & Business Univ Sch Informat & Elect Engn Yantai 264005 Peoples R China

The controller placement problem (CPP) is a key technical challenge in a large-scale Software Defined Network (SDN). Low-complexity heuristic algorithm is widely used for solving the CPP. However, parameter settings of the heuristic algorithm greatly affect the result of the CPP. Therefore, we establish a Parameter Optimization Model (POM) for the heuristic algorithm applied to the CPP. The heuristic algorithm can effectively solve the CPP by using the optimized parameters obtained in POM. To verify the effectiveness of the POM, we first establish a synthetical-delay controller placement model to reduce the delay between the controllers and the switches and the delay between the controllers. Further, we select the bat algorithm, the firefly algorithm, and the varna-based optimization respectively to solve the model, and use the particle swarm optimization method to optimize the parameters of the three algorithms. Experimental results on real topologies show that compared with original algorithms and other similar algorithms, the algorithms with optimized parameters perform better.

关键词： controller placement problem delay heuristic algorithm parameter optimization particle swarm optimization software defined network

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：